Effective Reinforcement Learning for Mobile Robots

Programming mobile robots can be a long, time-consuming process. Specifying the low-level mapping from sensors to actuators is prone to programmer misconceptions, and debugging such a mapping can be tedious. The idea of having a robot learn ow to accomplish a task, rather than being told explicitly is an appealing one. It seems easier and much more intuitive for the programmer to specify what he robot should be doing, and to let it learn the fine details of how to do it. In this paper, we introduce a framework for reinforcement learning on mobile robots and describe our experiments using it to learn simple tasks.